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<Paper uid="W04-2413">
  <Title>Semantic Role Labelling With Chunk Sequences</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We describe a statistical approach to semantic role labelling that employs only shallow information. We use a Maximum Entropy learner, augmented by EM-based clustering to model the fit between a verb and its argument candidate. The instances to be classified are sequences of chunks that occur frequently as arguments in the training corpus. Our best model obtains an F score of 51.70 on the test set.</Paragraph>
  </Section>
class="xml-element"></Paper>